{"id":"W7139699290","doi":"","title":"Результати проспективного когортного дослідження ефективності алгоритму супроводу вагітностей у пацієнток з групи високого перинатального ризику щодо зменшення перинатальних втрат та покращення неонатального результату","year":2021,"lang":"uk","type":"article","venue":"The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prospective cohort study; Pregnancy; Cohort; Cohort study; Prenatal diagnosis; Congenital malformations; Perinatal mortality; Advanced maternal age; Prenatal care","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002573773,0.0005051204,0.0004523628,0.002846624,0.0008389278,0.002927156,0.0006176788,0.0006868342,0.01670584],"category_scores_gemma":[0.005989694,0.0006181605,0.0007303377,0.002066349,0.001441539,0.001387062,0.0009108116,0.001176315,0.003571079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175924,"about_ca_system_score_gemma":0.003081983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005959043,"about_ca_topic_score_gemma":0.007883081,"domain_scores_codex":[0.9978477,0.0005962595,0.000204039,0.0003211858,0.0008659187,0.0001649376],"domain_scores_gemma":[0.997277,0.0009451049,0.0005928428,0.0003048394,0.0007120955,0.0001680631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005300594,0.0001850764,0.07585639,0.001063233,0.000217213,0.002123674,0.002952768,0.003300573,0.02690029,0.1050908,0.012086,0.7696939],"study_design_scores_gemma":[0.0002856524,0.0006293346,0.262078,0.001234592,0.0006852726,0.01421351,0.004947774,0.01074312,0.03414404,0.1450986,0.5255108,0.0004293786],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3411719,0.05700174,0.3448802,0.01210959,0.002001405,0.0008745595,0.004284032,0.000720497,0.2369561],"genre_scores_gemma":[0.8039095,0.02014409,0.1414569,0.0004701877,0.0005572584,0.0007212835,0.001234249,0.0002290116,0.03127742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01670584,"threshold_uncertainty_score":0.05588663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09421846348127426,"score_gpt":0.3535321250262836,"score_spread":0.2593136615450093,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}